Data Anonymization Using Map Reduce On Cloud
1
Shivaprasad Goud &
2K.Ganeshwar
1
s V College Of Engineering
Assistant Professor S V College Of Engineering
ABSTRACT
A large number of cloud services require users to share private data like electronic health records for data analysis or mining, bringing privacy concerns. Anonymizing data sets via generalization to satisfy certain privacy requirements such as k-anonymity is a widely used category of privacy preserving techniques. At present, the scale of data in many cloud applications increases tremendously in accordance with the Big Data trend, thereby making it a challenge for commonly used software tools to capture, manage, and process such large-scale data within a tolerable elapsed time. As a result, it is a challenge for existing anonymization approaches to achieve privacy preservation on privacy-sensitive large-scale data sets due to their insufficiency of scalability. In this paper, we propose a scalable two-phase top-down specialization (TDS) approach to anonymize large-scale data sets using the Map Reduce framework on cloud. In both phases of our approach, we deliberately design a group of innovative Map Reduce jobs to concretely accomplish the specialization computation in a highly scalable way. Experimental evaluation results demonstrate that with our approach, the scalability and efficiency of TDS can be significantly improved over existing approaches.
KEYWORDS: Data anonymization; top-down specialization; MapReduce; cloud; privacy preservation
INTRODUCTION
Cloud computing is one of the most pre-dominant paradigm in recent trends for computing and storing purposes. Datasecurity and privacy of data is one of the major concern in the cloud computing.Dataanonymization has been extensivelystudied and widely adopted method for privacy preserving in data publishing and sharing methods. Data anonymizationispreventing showing up of sensitive data for owner’s data record to mitigate unidentified Risk. The privacy of individualcan be adequately maintained while some aggregate information is shared to data user for data analysis and data mining.The proposed method is generalized method data anonymization using Map Reduce on cloud. Here we Two Phase TopDown specialization. In First phase, original data set is partitioned into group of smaller dataset and they are anonymizedand intermediate result is
conducting experiments on real-world data sets.Experimental results show that with the approach, the scalability and efficiency of TDS can beimproved. It evaluates theapproach by conducting experiments on real-world data sets. Experimental results demonstrate that with the approach, thescalability and efficiency of TDS can be improved significantly over existing approaches. Themajor contributions of deliberately design a group of innovative MapReduce jobs to concretely accomplish the specializations in a highlyscalable fashion. Secondly, it propose a two-phase TDS approach to gain high scalability via allowing specializations toe conducted.
Fig 1:System architecture
In above figure software by way of a facility model provides three different functionalities such as Users, Providers and public providers so we are going to discuss about it mobile users will provide data outside to the private clouds which will store the filtered results on the public cloud. This model is supporting execution of private mechanisms and shifting storage data to the clouds which leave users with light tasks.
1.1 Cloud Computing
Capacity is picking up prevalence as of late. In big commercialsituations, we see the
ascensionrequiredafterward for
datasubcontracting, which assistances with the key administration of corporate information? It is
capacity to delegate the entrance privileges of the sharing information to others so they can get to these information from the server specifically. Expect that Alice puts all her private photographs on Drop box, and she wouldn't like to open her photographs to everybody. Because of different information spillage plausibility Alice can't feel assuaged by simply depending on the security insurance components gave by Drop box, so she encodes all the photographs utilizing her own keys before transferring. One day, Alice's companion, Bob, requests that her share the photographs assumed control over every one of these years Which Bob showed up.Alice can then utilize the offer capacity of Drop box; however the issue now is the means by which to delegate the unscrambling rights for these photographs to Bob. A conceivable alternative Alice can pick is to safely send Bob the mystery keys included. Actually, there are two great courses for her under the customary encryption ideal model:
1. Alice scrambles all records with a solitary encryption key and gives Bob the comparing mystery key straightforwardly.
2. Alice scrambles records with particular keys and sends Bob the relating mystery keys.
1.2 Problem Definition
This task points that productivity disadvantages of the most existing ABE plans is that unscrambling is extravagant for asset restricted gadgets because of blending operations, and the quantity of matching operations needed to unscramble a figure content develops with the intricacy of the entrance arrangement.
The above perception spurs us to study ABE with unquestionable outsourced unscrambling in this theory work. Here stressed that an ABE plan with
secure outsourced unscrambling does not so much ensure certainty.
2. SYSTEM ANALYSIS
2.1 Feasibility Study
Feasibility study is ansignificantstage in the software development procedure. It empowers the designer to have an appraisal of the item being produced. It alludes to the possibility investigation of the item regarding results of the item, operational utilization and specialized backing needed for executing it. Feasibility study ought to be performed on the premise of different criteria and parameters. The different feasibility studies are:
Operational feasibility
Technical feasibility
Economic feasibility
2.1.1 Operational Feasibility
The feature of learning is to check the level of receiving of the scheme by the customer. This comprises the procedure of working out the user to use the system efficiently. The user must not feel threatened by the system, instead must accept it as a necessity. His level of confidence must be raised so that he is also able to make some constructive criticism, which is welcomed, as he is the final user of the system. Here instead of sending multiple keys we are aggregating the multiple keys into a single key. By aggregating the keys it is very easy to manage keys.
2.1.2 Technical Feasibility
created must not have popularity on the accessible specialized assets. This will prompt levels of popularity on the accessible specialized assets. This will prompt levels of popularity being set on the customer. The created framework must have an unobtrusive necessity, as just insignificant or invalid changes are needed for executing this framework. The technology we used is java. Java is robust and it follows that write once run anywhere. Java provides security it is flat form independent.
2.1.3 Economic Feasibility
This study is done to check the monetary effect that the framework will have on the association. The measure of store that the organization can fill the innovative work of the framework is restricted. The consumptions must be advocated.
The admin uploads the files. When admin is uploading files to user he is encrypting the files. And he shared encrypted files to the users. The encrypted key is send to the user’s mail and when they want to download the files and he has to enter the key.
CONCLUSION
Privacy preserving data analysis and data publishing are becoming serious problems in today’s ongoing world. That’s why different approaches of data anonymization techniques are proposed. To the best of our knowledge, TDS approach using MapReduce are applied on cloud to data anonymization and deliberately designed a group of innovative Map Reduce jobs to concretely accomplish the specialization computation in a highly scalable way.
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shivaprasad goud
S V COLLEGE OF ENGINEERING
K.GANESHWAR
Assistant Professor
